CN111565372B - Directed sensor network optimized deployment system and method - Google Patents
Directed sensor network optimized deployment system and method Download PDFInfo
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Abstract
The invention discloses a system and a method for optimizing deployment of a directed sensor network, which solve the defects of large calculated amount, low searching speed and high complexity of the existing directed sensor network. The deployment system is sequentially connected end to end by an initialization random deployment module, a weighted discretization module, a minimum exposure path searching module and an optimization deployment module, so that the coverage quality of a deployment area is improved. The deployment method comprises the following steps: determining a deployment area, randomly and initially deploying the sensor, and setting a minimum exposure target value; searching a minimum exposure path by using a particle swarm algorithm; determining an optimal deployment point and deploying a directional sensor; and obtaining an optimal deployment scheme. The method uses the particle swarm algorithm to search the minimum exposure path, finds the optimal deployment point and deploys the directional sensor, effectively improves the minimum exposure path, improves the deployment area coverage rate, has high search speed, simple and convenient calculation and high efficiency, and is used for the optimized deployment of the directional sensor network.
Description
Technical Field
The invention belongs to the technical field of information, mainly relates to an optimized deployment problem of a wireless sensor network, and particularly relates to an optimized deployment system and method of a directed sensor network.
Background
A large-scale Wireless Sensor Network (WSN) is one of the hot problems in the field of information technology. When environment monitoring and information perception are needed to be carried out on a certain area, the wireless sensor network needs to be deployed in the area in a large scale, and perception of various intrusion behaviors in the deployed area can be achieved, so that the large-scale wireless sensor network is wide in application range and can be applied to the fields of military, medicine, agriculture, weather and the like to complete a plurality of difficult tasks. However, the biggest challenge faced by WSN is the deployment of sensors, which will affect the characteristics of the entire network, and directly affect the coverage rate, network connectivity, network energy efficiency, system lifetime, etc. of the network, it is necessary to optimize the coverage capability of the system on the basis of ensuring the system detection efficiency.
Existing sensors mostly use an omni-directional sensing model without directional limitation, i.e. the sensing capability of the sensor in all directions is the same. However, some commonly used sensors (e.g., camera sensors and doppler probes, etc.) are directional, and when the sensors conform to a directional sensing model, the sensor network is referred to as a directional sensor network. Compared with the traditional sensor network, the problem of optimizing and deploying the directional sensor network is more complex, and because the sensing area of the directional sensor network is the sensing range in a specific direction limited by the detection visual angle, the sensing area is different from an omnidirectional sensing model in many aspects, the existing omnidirectional deploying method cannot be directly applied to the directional sensor network. The existing research on the optimized deployment of the omni-directional and directional sensor networks is summarized as follows: lili Zhang et al, published in 2015seven International Symposium on Parallel architecture, in a text entitled "The minimum exposure path in mobile wireless sensor networks" by Algorithms and programing, performs optimal deployment of a sensor network based on a Voronoi diagram, and proposes a tangent evasion method to search for a minimum exposure path, which has The advantage of simultaneously realizing solution of The minimum exposure path of a mobile sensor and a static sensor in a deployment area, but an omnidirectional sensor model is used herein and cannot be applied to a directed sensor network; liang Liu et al, published in the text of "minimum exposure path algorithms for direct sensor networks" of Wireless Communications And Mobile Computing, propose a directed sensor network optimization deployment method based on a Voronoi graph of sector centroid, calculate a minimum exposure path by using Dijkstra shortest path algorithm, And then directly add sensor nodes along the minimum exposure path, which can effectively improve the coverage quality, but have the disadvantages that the weighted discretization operation process using the Voronoi graph is cumbersome And complex in calculation, And directly add nodes along the minimum exposure path can cause node redundancy problems, for example; hao Feng et al, published in a text entitled "A novel minor exposure path layout in wireless Sensor Networks and its solutions algorithms" of International Journal of Distributed Sensor Networks, calculates a minimum exposure path using a Voronoi diagram and a finite difference method, decomposes a section of curve path into a plurality of segments, then solves the minimum exposure path respectively, finally summarizes the minimum exposure paths to obtain a final minimum exposure path, creatively calculates the minimum exposure path using the finite difference method, but has the disadvantages of large calculated amount and slow processing speed; binh N et al, in 2017IEEE Symposium Series on computer Intelligent Association, "Genetic algorithm for solving minor exposure path in mobile sensor networks", use Genetic algorithm to find the minimum exposure path of the directed sensor network, through initializing the population, use cross and mutation method to generate new next generation to join in the population, iterate until finding the minimum exposure path satisfying the conditions, this method can find the minimum exposure path effectively, but its disadvantage lies in that the convergence speed of Genetic algorithm is slow, the calculated amount is relatively large.
The advantages and disadvantages of existing omni-directional and directional sensor networks are summarized as follows:
the existing sensor network mostly uses omnidirectional sensors without direction limitation, but some commonly used sensors are all directional sensors at present, so the disadvantage is that the omnidirectional sensor optimized deployment scheme cannot be directly applied to the directional sensor network.
The existing directed sensor network optimization deployment problem is that the minimum exposure path is calculated by performing weighted discretization operation on a Voronoi diagram of a directed sensing model, and the defects are that the weighted discretization process of the Voronoi diagram is complex, the searching speed is slow, and the calculation amount is large.
After the minimum exposure path is obtained in the existing directed sensor network optimization deployment problem, a further optimization deployment scheme is not generally provided, or deployment nodes are simply and directly added along the minimum exposure path, which easily causes the problems of node redundancy and over deployment.
Disclosure of Invention
The invention aims to provide an optimized deployment system and method of a directional sensor network, which have the advantages of high convergence rate, high search speed, simple and convenient calculation and capability of effectively improving the coverage rate of a deployment area, aiming at the problems and the defects of the conventional optimized deployment scheme of an omnidirectional sensor and a directional sensor network.
The invention provides a directed sensor network optimized deployment system, which comprises: the method comprises the steps that a random deployment module and an optimized deployment module are initialized, the random deployment module is initialized to collect environmental information of a deployment area and determine an area suitable for deployment, and the optimized deployment module outputs an optimal deployment scheme of the directed sensor network; between the two modules, there is an optimized deployment preprocessing link, which is characterized in that: the optimized deployment preprocessing link comprises a weighted discretization module and a minimum exposure path searching module; the initialization random deployment module, the weighted discretization module, the minimum exposure path searching module and the optimized deployment module are sequentially connected end to end; the method comprises the steps that an initialization random deployment module abstracts a sensor deployment area into a rectangular area, directional sensors are initially and randomly deployed in the area, and deployment position information of each directional sensor is input into a weighted discretization module after deployment is finished; the weighted discretization module divides the deployment area into fine grids, generates a weighted undirected graph, calculates the exposure and transmits the exposure information to the minimum exposure path searching module; the minimum exposure path searching module searches a minimum exposure path by using a particle swarm optimization algorithm, performs discretization operation on the minimum exposure path and transmits the discretization operation to the optimized deployment module; and the optimal deployment module finds a grid center point corresponding to the line segment with the minimum exposure and sets the grid center point as an optimal deployment point, relocates the directional sensor to the point, and calculates the minimum exposure value of the relocated system until the value meets a set target value to obtain an optimal deployment scheme of the directional sensor network.
The invention also provides a directed sensor network optimized deployment method, which is realized on the directed sensor network optimized deployment system and is characterized by comprising the following steps:
1) determining a deployment area, randomly and initially deploying the sensor in the deployment area, and setting a minimum exposure target value: collecting environmental information of an area to be deployed, determining an area where the directional sensor is deployed, called a deployment area, randomly determining a position where the directional sensor is deployed in the area, called an initial position, and deploying the directional sensor at the initial position; discretizing the deployment area by using a grid method, and generating a weighted undirected graph in the deployment area; setting a minimum exposure target value according to the deployment area as a judgment basis for obtaining an optimal deployment scheme;
2) searching for a minimum exposure path using a particle swarm optimization algorithm: searching a minimum exposure path in a deployment area by using a particle swarm algorithm, wherein each particle is represented by a velocity vector and a position vector, and the two vectors are initialized to be random vectors in a solution space dimension; continuously updating the speed vector and the position vector of each particle towards the local optimal solution and the global optimal solution, and simultaneously calculating the exposure by using an exposure formula until a minimum exposure path is found;
3) calculating the exposure value of the line segment where the grid point is located on the minimum exposure path: after the minimum exposure path is obtained in the step 2), calculating the exposure values of the path line segments of all the grid points on the minimum exposure path by using an exposure formula;
4) determining an optimal deployment point: comparing the exposure values of the path line segments of all the grid points obtained in the step 3), finding out the line segment with the minimum exposure value, and determining the grid center point corresponding to the line segment as the optimal deployment point on the minimum exposure path;
5) adjusting the deployment of the sensors: deploying a directional sensor at the optimal deployment point on the minimum exposure path obtained in the step 4);
6) obtaining an optimal deployment scheme: calculating the minimum exposure value of the minimum exposure path after the directional sensor is deployed in the step 5), and comparing the minimum exposure value with the target value set in the step 1); if the minimum exposure value does not meet the set target value, returning to the step 2) to continue running; and if the minimum exposure value meets the set target value, obtaining the optimal deployment scheme of the directed sensor network.
The invention provides a directed sensor network optimized deployment system and a directed sensor network optimized deployment method, which optimize the position of a directed sensor after initial deployment based on a minimum exposure path, thereby reducing blind spots, improving the minimum exposure path, maximizing the coverage, reducing the probability of missed detection and realizing the improvement of the coverage quality of the system.
Compared with the prior art, the invention has the following advantages:
is easier to realize, and can effectively improve the coverage quality: the system and the method for optimizing and deploying the directed sensor network are based on the sector sensing model, compared with the traditional omnidirectional sensor, the system and the method have stronger detection capability in a specific direction, compared with the existing directed sensor network optimizing and deploying technology, the system and the method have the advantages of simple steps, low system complexity and easiness in implementation, and can effectively improve the coverage efficiency and the coverage quality.
The particle swarm algorithm has high convergence speed and high efficiency: the scheme of the invention uses the particle swarm algorithm to search the minimum exposure path, each particle in the algorithm is represented by a speed vector and a position vector, the convergence speed is higher, the search speed is faster, the efficiency is high, and the algorithm is simple.
Grid weighting discretization, simple and convenient calculation: in the scheme of the invention, the exposure is calculated by using the full sensor intensity function and the maximum sensor intensity function, so that a weighted undirected graph is generated, discretization of the deployment region is realized by using grid division, the problem of the minimum exposure path of a continuous domain is converted into the problem of the shortest path of a discrete domain for solving, and the calculation is simpler and more convenient.
Finding an optimal deployment point, and reducing node redundancy: according to the scheme, the optimal deployment point is found based on the line segment with the minimum exposure value on the minimum exposure path, the sensor is deployed at the point to improve the coverage quality, and the node redundancy problem is effectively reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a block flow diagram of the present invention;
FIG. 3 is a schematic diagram of deployment area meshing in accordance with the present invention;
FIG. 4 is a schematic undirected graph of path weighting generated by the meshing of the present invention;
FIG. 5 is a schematic diagram of particle movement in the minimum exposure path search of the present invention;
FIG. 6 is a schematic diagram of a deployment scenario of the present invention, wherein FIG. 6(a) is a schematic diagram of searching a minimum exposure path, and FIG. 6(b) is a schematic diagram of deploying an optimal deployment point;
fig. 7 is a diagram of simulation results of the deployment scenario of the present invention, in which fig. 7(a) is a minimum exposure path at a sector sensing angle of 45 °, fig. 7(b) is a minimum exposure path at a sector sensing angle of 90 °, fig. 7(c) is a minimum exposure path at a sector sensing angle of 120 °, and fig. 7(d) is a minimum exposure path after optimal deployment for an optimal deployment point.
Detailed Description
The invention will be described in detail below with reference to the following figures and examples:
example 1:
the large-scale sensor network is a research hotspot, and can be applied to various fields closely related to human life, such as military, medicine, agriculture, meteorology and the like, because the large-scale sensor network can sense various intrusion behaviors in a deployment area, and the deployment problem is a main problem of the sensor network and can affect the characteristics and sensing capability of the whole network. Existing sensors mostly use an omnidirectional sensing model without direction limitation, but some common sensors have directionality such as a camera sensor and a doppler probe, etc., which sense sensitivity in a specific direction is superior to other directions. The directional sensor in the present invention may specifically be: directional sensors such as microwave radar, infrared, and ultrasonic. The existing research on optimizing and deploying the directed sensor network mainly focuses on deploying the sensor nodes limited by energy consumption to realize simple environmental data acquisition, processing and transmission, and the performance of the sensor network is often optimized by using a minimum exposure path. In the prior art, the solution of the minimum exposure path is converted into a discrete domain solution through a weighted discretization operation, and a common method is based on Voronoi diagram weighted discretization, but the discretization method is complex in calculation and directly causes low search speed and low efficiency; in the prior art, a genetic algorithm is also commonly used for searching the minimum exposure path, but the genetic algorithm has the defects of low convergence rate and large calculation amount.
The invention develops research aiming at the defects in the prior art and provides a directed sensor network optimal deployment system, which comprises the following components: the method comprises the steps that a random deployment module and an optimized deployment module are initialized, the random deployment module is initialized to collect environmental information of a deployment area and determine an area suitable for deployment, and the optimized deployment module outputs an optimal deployment scheme of the directed sensor network; an optimized deployment preprocessing link is arranged between the two modules. Referring to fig. 1, fig. 1 is a schematic diagram of the system configuration of the present invention, and the optimized deployment preprocessing link of the present invention includes a weighted discretization module and a minimum exposure path search module; the device comprises an initialization random deployment module, a weighted discretization module, a minimum exposure path searching module and an optimization deployment module which are sequentially connected end to end. The method comprises the steps that an initialization random deployment module abstracts a sensor deployment area into a rectangular area, directional sensors are initially and randomly deployed in the area, and deployment position information of each directional sensor is input into a weighted discretization module after deployment is finished; the weighted discretization module divides the deployment area into grids with controllable fineness and generates a weighted undirected graph, the exposure formula is utilized to calculate the exposure and the exposure information is transmitted to the minimum exposure path searching module; the minimum exposure path searching module searches a minimum exposure path by using a particle swarm optimization algorithm with high convergence rate and simple and convenient calculation, performs discretization operation on the minimum exposure path and transmits the discretization operation to the optimized deployment module; and the optimal deployment module calculates by using an exposure formula to find a line segment with the minimum exposure, a grid central point corresponding to the line segment is set as an optimal deployment point, the directional sensor is redeployed at the point, and the minimum exposure value of the redeployed system is calculated until the value meets a set target value, so that an optimal deployment scheme of the directional sensor network is obtained. The optimal deployment scheme can effectively improve the coverage rate of the directed sensor network, and finally realize the improvement of the performance of the sensing network.
The existing directed sensor network is usually optimized and deployed based on a minimum exposure path, but the problems of large calculation amount, slow search speed, low efficiency and the like exist in the search process of the minimum exposure path, and meanwhile, the problems of node redundancy and over-deployment also occur. Aiming at the problems, after the directional sensor is randomly and initially deployed, the weighted discretization module is added, and a weighted undirected graph is generated by weighted discretization of the deployment area, so that the calculation amount and the calculation complexity are greatly reduced; meanwhile, the minimum exposure path searching module is added, and the minimum exposure path is searched by using a particle swarm algorithm with high convergence speed, simple and convenient calculation and high efficiency, so that the searching time is saved; and an optimal deployment point is found for deployment based on the minimum exposure path, so that the problem of node redundancy is reduced, and the coverage quality in a deployment area is effectively improved.
Example 2:
the overall composition of the directed sensor network optimized deployment system is the same as that in embodiment 1, a weighted discretization module divides a sensor deployment area into a fine grid, and a detection target moves along the grid; the weighted discretization module receives the deployment position information of each sensor from the initialized random deployment module, and generates a weighted undirected graph according to the deployment position information of each sensor, wherein the edge weight value of the weighted undirected graph is an exposure value on a grid path; and calculating the exposure on the grid moving path through an exposure formula, and outputting the exposure information to a minimum exposure path searching module.
The fine degree of the fine grid is controllable, corresponding adjustment can be made along with the size of the deployment area and the size of the sensing range of the sensor, and the default fine grid is to a meter level. For example, when the sensor sensing range is 0-5 meters, the deployment area meshing unit is 1 meter, when the sensor sensing range is 5-10 meters, the deployment area meshing unit is 2 meters, and so on.
The discretization of the deployment area by using the weighted undirected graph is because in the sensing area of the actual sensor, the specific sensing range boundary of each sensor needs to be determined, and meanwhile, in the process of solving the whole exposure light path, the coverage field intensity in each grid can be obtained through the weighted undirected graph.
Example 3:
the directed sensor network optimized deployment system is the same as the embodiment 1-2, and the minimum exposure path searching module in the invention adopts the particle swarm optimization algorithm to search the minimum exposure path, wherein each particle is represented by a speed vector and a position vector; the two vectors are initialized to random vectors in the solution space dimension; in the iterative process of the algorithm, the particles continuously update the speed and the position of the particles towards the local optimal solution and the global optimal solution; continuously calculating the exposure by using an exposure formula until a path with the minimum total exposure is found, and calling the path with the minimum total exposure as a minimum exposure path; and outputting the dispersed minimum exposure path information to an optimized deployment module.
In the invention, the discrete minimum exposure path is used for calculation, and the minimum exposure path problem can be converted into the shortest path problem, so that the calculation amount is greatly reduced, and the calculation is easy; and further generating an optimal deployment scheme after the optimal deployment point is found by the optimal deployment module, thereby improving the coverage rate of the deployment area.
The particle swarm algorithm in the minimum exposure path searching module is used as a bionic algorithm and has wide application in the fields of target seeking, database training and the like. Meanwhile, the particle swarm algorithm obtains the global optimal solution by utilizing information sharing and cooperation among the groups, and has the characteristics of high convergence rate, high search speed, high efficiency and simple algorithm, so that the particle swarm algorithm is used for searching the minimum exposure path, the search speed can be effectively improved, and the high-efficiency search of the minimum exposure path is realized.
Example 4:
the directed sensor network optimized deployment system is the same as the embodiment 1-3, and the optimized deployment module obtains the minimum exposure path information from the minimum exposure path searching module; comparing the exposure values of the line segments corresponding to all the grid points to obtain a line segment with the minimum exposure in the minimum exposure path, wherein the grid center point corresponding to the line segment is set as an optimal deployment point; and redeploying the directional sensor at the optimal deployment point, continuously calculating the minimum exposure value of the system until the value meets the set target value, and outputting the optimal deployment scheme of the directional sensor network.
After the minimum exposure path in the deployment area is obtained, the coverage quality of the sensor network is improved by finding the optimal deployment point and deploying the directional sensor at the point, and the network coverage rate and the detection capability are improved. The optimal deployment point is found to optimize the sensor network, so that the problems that after the minimum exposure path is obtained in the prior art, nodes are directly added along the minimum exposure path, node redundancy and over deployment are easily caused are effectively solved.
Example 5:
the invention is also a directed sensor network optimized deployment method, which is implemented on any one of the above directed sensor network optimized deployment systems, see fig. 2, where fig. 2 is a flow chart of the invention, and includes the following steps:
1) determining a deployment area, randomly and initially deploying the sensor in the deployment area, and setting a minimum exposure target value:
1a) an initialization random deployment module in the system collects environmental information of an area to be deployed, determines an area where directional sensors are deployed, called a deployment area, randomly determines the position where the directional sensors are deployed in the area, called an initial position, and deploys the directional sensors at the initial position;
1b) initializing a weighted discretization module in the system, discretizing a deployment area by using a grid method, and generating a weighted undirected graph in the deployment area;
1c) the minimum exposure target value is set according to the size of the deployment area, the setting of the value is related to the deployment area, the sensing range of the sensors and the number of the sensors, and a real number is usually set according to specific conditions and is used as a judgment basis for obtaining an optimal deployment scheme.
2) Searching for a minimum exposure path using a particle swarm optimization algorithm:
2a) a minimum exposure path searching module in the system searches a minimum exposure path in a deployment area by using a particle swarm algorithm, each particle is represented by a velocity vector and a position vector, and the two vectors are initialized to be random vectors in a solution space dimension;
2b) in the particle swarm optimization iteration process, each particle continuously updates the speed vector and the position vector of the particle towards the local optimal solution and the global optimal solution, and meanwhile, the exposure degree is calculated by using an exposure degree formula until a minimum exposure path is found.
3) The optimized deployment module determines an optimal deployment point:
3a) suppose that v is a certain grid point in the minimum exposure path obtained in step 2)i，i＝1，2，……，imax，imaxCalculating to obtain an exposure value of a line segment corresponding to the point by using an exposure formula for the maximum value in the serial numbers of the divided grids;
3b) and calculating the exposure values of the line segments corresponding to all the grid points in the minimum exposure path, comparing the sizes of the line segments, and finding out the line segment with the minimum exposure value, wherein the central point of the grid corresponding to the line segment is the optimal deployment point obtained at this time.
4) Adjusting sensor deployment:
deploying a directional sensor at the optimal deployment point on the minimum exposure path obtained in the step 3 b).
5) Obtaining an optimal deployment scheme:
calculating the minimum exposure value of the minimum exposure path after the directional sensor is deployed in the step 4), and comparing the minimum exposure value with the target value set in the step 1); if the minimum exposure value does not meet the set target value, returning to the step 2) to continuously search for the optimal deployment scheme; if the minimum exposure value meets a set target value, obtaining an optimal deployment scheme of the directed sensor network;
the number of times the directional sensors are deployed depends on the set minimum exposure target value, and the number of redeployed directional sensors is generally required to be less than or equal to the number of initially deployed sensors in the deployment area.
Starting from the minimum exposure path, the particle swarm algorithm is firstly used for searching the minimum exposure path, so that the defects of low searching speed, low convergence speed, low efficiency and the like caused by the fact that the genetic algorithm and the Voronoi diagram-based minimum exposure path are used for searching in the prior art are overcome. And further finding an optimal deployment point on the minimum exposure path, and deploying the directional sensor at the optimal deployment point to improve the coverage rate and the coverage quality of a deployment area. Through the steps, the coverage rate in the deployment area is effectively improved.
Example 6:
as in embodiments 1 to 5, the discretization of the deployment area in step 1) of the present invention refers to dividing the deployment area into fine grids, then numbering the grids, where the numbering order is according to the upper-lower, left-right, and g (i) grid representing the ith grid, where i is 1,2, … …, i is 1max，imaxIs the maximum value among the number of the divided grids.
Referring to fig. 3, fig. 3 is a schematic diagram of deployment area meshing according to the present invention. In the invention, a moving object is supposed to move along the central point of a grid in a deployment area and is called as a detection target; the fine mesh is divided according to fig. 3, and assuming that the detection target moves along the center point of the mesh, the broken line in fig. 3 is the moving track of the detection target.
The fineness degree of the fine grid can be adjusted correspondingly along with the size of a deployment area and the size of a sensing range of the sensor, and the fineness is up to a meter level by default. If the coverage area of the grid by the sensing range of any directional sensor node S in the deployment area exceeds half, the grid is considered to be covered.
Example 7:
the directed sensor network optimized deployment system and method are the same as embodiments 1-6, and the weighted undirected graph in step 1) of the invention refers to a method for connecting any grid center point in a deployment area with grid center points in eight adjacent directions, and the exposure is used as an edge weight of a line segment after the connection.
Referring to FIG. 4, FIG. 4 is a schematic view of the path weighting undirected generated by the mesh division of the present invention, wherein g: (b)1) G to g: (9) Is 9 grids, and the central points of the grids are respectively defined as v1To v9. The weighted undirected graph is denoted as G ═ V, E, where V denotes a set of grid center points, and E denotes a connection line of the grid center points in eight adjacent directions, that is, a discretization path where the detection target moves in the area grid; the edge weight of the weighted undirected graph G is the exposure on the grid path.
By drawing the weighted undirected graph, the problem of the minimum exposure path is converted into a discrete domain from a continuous domain, and the problem of the minimum exposure path is converted into the problem of the shortest path to solve, so that the calculation is greatly simplified, and the method is favorable for quickly searching the minimum exposure path.
Example 8:
the system and the method for optimizing and deploying the directed sensor network are the same as the embodiments 1 to 7, and the exposure calculation in the step 2) of the invention is represented by the field intensity of the directed sensor in the deployment area at any grid point and the path line length corresponding to the grid point, wherein the field intensity has two representing methods, namely the coverage field intensity of the whole sensor and the maximum coverage intensity of the sensor.
By using two different sensor field intensity functions, different representation modes of the field intensity can be compared, and the influence of different field intensity representation methods on the coverage quality of the deployment area can be further checked.
The optimized deployment method of the directed sensor network comprises the steps of discretizing a deployment area, initially and randomly deploying the directed sensors in the discretized area, searching a minimum exposure path of the directed sensor network by using a particle swarm optimization algorithm, and redeploying the sensor network by using an optimized deployment strategy according to the searched minimum exposure path so as to improve the coverage quality of the whole sensor network.
A more detailed example is given below to further illustrate the present invention.
Example 9:
the system and the method for optimizing and deploying the directed sensor network are the same as those in embodiments 1 to 8, and the system and the method for optimizing and deploying the directed sensor network designed by the invention are as follows:
abstracting a deployment area into a rectangular area, and initially deploying the directional sensor at random after discretizing the deployment area; finding a minimum exposure path by using a minimum exposure path search algorithm, and converting the minimum exposure path problem from a continuous domain into a discrete domain; and aiming at the searched minimum exposure path, optimally deploying the sensor network by using an optimal deployment strategy to improve the coverage quality of the whole sensor network.
Furthermore, the discretization of the deployment area divides the sensor deployment area into fine grids with controllable fineness, utilizes a weighted path undirected graph to carry out edge weight calculation, and weights are given to all edges, so that the grids are converted into weighted graphs, detection targets move along the grids, and the calculation is simpler and more convenient.
Further, the minimum exposure path search of the present invention employs a particle swarm optimization algorithm, wherein each particle is represented by a velocity vector and a position vector. The two vectors are initialized to be random vectors in a solution space dimension, in the algorithm iteration process, the particles continuously update the speed and the position of the particles towards the current local and global optimal solutions, meanwhile, the exposure is continuously calculated until a path is found, so that the total exposure obtained from the sensor is the minimum, the path is the minimum exposure path, and the particle swarm algorithm is used for searching the minimum exposure path, and the method has the advantages of high convergence speed and high efficiency.
Furthermore, the sensor network redeployment scheme of the invention is that on the basis of the original minimum exposure path, the line segment with the minimum exposure is obtained by traversing the exposure of the line segments corresponding to all the grid points, the central point of the grid corresponding to the line segment is the optimal deployment point obtained at this time, and the sensor equipment is redeployed at the point, so that the minimum exposure path is improved, and the sensor is deployed by finding the optimal deployment point, so that the node redundancy is greatly reduced.
The method comprises the following implementation steps:
The optimized deployment method of the directed sensor network firstly utilizes the initialized random deployment module to divide a sensor deployment area into a fine grid.
Referring to fig. 3, fig. 3 is a schematic diagram of deployment area meshing, taking 10 meters as an example of deployment area side length, a deployment area with 10 meters side length is divided into 10 × 10 small meshes, and the unit of the mesh is accurate to 1 meter. After the mesh division of the area is completed, all meshes in the whole area are numbered, and the mesh with the number g (i) represents the ith mesh (i is 1,2, … …, i) in the graph according to the numbering order of the mesh with the number g (i)max，imaxThe maximum value among the number of the divided grids). For any g (i), if the sensing range of the sensor node S covers more than half of the area, the grid is marked to be covered, namely, the sensing can be carried out.
And 2, calculating the path curve exposure.
Assume that the target is from an initial point o in the deployment zone ZsTo the end point oeThe movement path of (a) cuts the deployment region grid into several sections denoted as o (i) (1, 2max) Where i denotes the number of the meshes passed in the path, imaxIs the maximum value in the grid. The path curve o of the target in the deployment zone Z can thus be determineds,oe]The exposure levels of (A) and (B) are as follows:
this formula is the exposure calculation formula in the present invention.
And 3, calculating the edge weight of the weighted undirected graph G.
In order to search for the minimum exposure path, a path-weighted undirected graph needs to be obtained first.
Referring to FIG. 4, FIG. 4 is a schematic undirected graph of path weighting generated by mesh division, wherein g: (1) G to g: (9) Is 9 grids, the central points of the grids are respectively defined asv1To v9. The weighted undirected graph G is represented as G ═ V, E, where V represents a set of grid center points, E represents a connection line of the grid center points in eight adjacent directions, i.e., a discretization path where the detection target moves in the area grid, and the edge weight of the weighted undirected graph G is the exposure calculated by using the exposure formula on the grid path.
And 4, performing particle swarm optimization search algorithm of the minimum exposure path.
The invention searches for a minimum exposure path based on a particle swarm optimization algorithm in which a population of particles is represented as a possible solution to the population and each particle is associated with two vectors: velocity vectors and position vectors, initially for each particle, are initialized to random vectors in the solution space dimension.
Referring to fig. 5, fig. 5 is a schematic diagram of particle movement in the minimum exposure path search according to the present invention, in the algorithm iteration process, the particles update their own speed and position through the following formula, and continuously move, and continuously calculate the exposure of the updated path at the same time, until the minimum exposure path is found, so that the total exposure obtained from the sensor is the minimum.
Wherein k represents the number of particle swarm iterations; omega is a weight factor; c. C1、c2The learning factors with the same value are also called acceleration constants and are used for speed updating in the particle swarm; r is1、r2Represents [0,1 ]]Uniform random numbers with the same value are taken in the range;
representing the optimal value searched by the current particle;
representing the optimal value searched by the whole population;
represents the moving speed of the particles;
the individual parameters of the particles, i.e., the values of the decision variables, are all parameters used in the particle swarm optimization of the present invention.
And 5, performing directed sensor node optimized deployment aiming at the minimum exposure path.
After the minimum exposure path search is completed, the directed sensor nodes are optimally deployed according to actual requirements and coverage requirements, the coverage quality of the targets in the deployment area is continuously improved, and the exposure value of the monitored targets is improved to the maximum extent.
Referring to fig. 6, a schematic diagram of the deployment scenario of the present invention is shown. Assuming a deployment area side length of 20 meters, the deployment area is divided into 20 × 20 grid areas, and the numbers in each square represent the coverage field strength. Assuming that a starting point and an end point are known, searching for a minimum exposure path by using a minimum exposure path algorithm, referring to fig. 6(a), where fig. 6(a) is a schematic diagram of searching for a minimum exposure path, and a zigzag line in fig. 6(a) represents the searched minimum exposure path; referring to fig. 6(b), fig. 6(b) is a schematic diagram of optimal deployment and deployment, and a sector area in fig. 6(b) represents the found optimal deployment point and deploys a directional sensor. The specific process is as follows:
5.1) determining a deployment area and randomly and initially deploying the sensors in the deployment area: collecting environmental information of an area to be deployed, determining an area where the directional sensor is deployed, called a deployment area, randomly determining a position where the directional sensor is deployed in the area, called an initial position, and deploying the directional sensor at the initial position; the deployment region is discretized using a grid method and a weighted undirected graph is generated within the deployment region.
5.2) searching a minimum exposure path by using a particle swarm optimization algorithm: searching a minimum exposure path in a deployment area by using a particle swarm algorithm, wherein each particle is represented by a velocity vector and a position vector, and the two vectors are initialized to be random vectors in a solution space dimension; each particle continuously updates its velocity vector and position vector towards the local and global optimal solutions, while calculating the exposure using the exposure formula until a minimum exposure path is found.
5.3) determining an optimal deployment point: let v be a grid point in the minimum exposure path obtained in step 5.2)i(i＝1，2，……，imax，imaxThe maximum value in the serial numbers of the divided grids), calculating by using an exposure formula to obtain an exposure value of the line segment corresponding to the point; and calculating the exposure values of the line segments corresponding to all the grid points in the minimum exposure path, comparing the sizes of the line segments, and finding out the line segment with the minimum exposure value, wherein the central point of the grid corresponding to the line segment is the optimal deployment point obtained at this time.
5.4) adjusting the deployment of the sensors: deploying a directional sensor at the optimal deployment point obtained in the step 5.3).
5.5) obtaining an optimal deployment scheme: calculating the minimum exposure value of the minimum exposure path after the directional sensor is deployed in the step 5.4), and comparing the minimum exposure value with the target value set in the step 5.1); if the minimum exposure value does not meet the set target value, returning to the step 5.2) to continue running; and if the minimum exposure value meets the set target value, obtaining the optimal deployment scheme of the directed sensor network.
The invention mainly solves the defects of large calculated amount, low searching speed and high complexity of the existing directed sensor network. The system comprises an initialization random deployment module, a weighted discretization module, a minimum exposure path searching module and an optimization deployment module; the initialization random deployment module abstracts a sensor deployment area into a rectangular area, initially randomly deploys directional sensors in the area, and sets a minimum exposure value according to the deployment area; the weighted discretization module divides the deployment area into fine grids and generates a weighted undirected graph; the minimum exposure path searching module searches a minimum exposure path by using a particle swarm algorithm; and the optimized deployment module finds an optimal deployment point on the minimum exposure path and deploys the directional sensor, and calculates the minimum exposure value after deployment until the minimum exposure value meets a set target, so as to obtain an optimal deployment scheme of the directional sensor network. The invention effectively improves the minimum exposure path, improves the coverage rate of the deployment area, and has the advantages of high search speed, simple and convenient calculation and high efficiency.
The technical effect of the invention is explained in a verification way by combining simulation data and results thereof as follows:
example 10:
the system and the method for optimizing and deploying the directed sensor network are the same as the embodiments 1 to 9.
Simulation conditions and content
Aiming at the design process of the minimum exposure path searching method and the optimized deployment strategy, the related performance of the directed sensor optimized deployment system and method is verified through experimental simulation, and the experimental contents are as follows: (1) verifying the particle swarm minimum exposure path search experiment through an experiment; (2) the verification node optimizes the deployment process, so that the coverage quality is improved. In a simulation experiment, Java language is adopted to complete corresponding simulation program codes, debugging and running of the codes are performed through extensible development platform Eclipse application software based on the Java language, and finally, corresponding simulation results are obtained. The directional sensor in the experiment takes a sector sensing model as an example, and can be a microwave radar, an infrared sensor, an ultrasonic sensor and the like in practice. The experiment setting content is shown in table 1, and table 1 shows the parameter content set for the simulation experiment:
table 1 parameter content of simulation experiment setup
| Serial | Setting content | |
| 1 | The directed sensor nodes are initially deployed randomly; | |
| 2 | directed transmissionThe sensor nodes have a self-positioning function; | |
| 3 | the sensing angles of the nodes of the directional sensor are respectively set to be 45 degrees, 90 degrees and 120 degrees. | |
| 4 | The deployment area is a square area with the side length of 25 m; |
results and analysis
Experimental results referring to fig. 7, fig. 7 is a diagram showing simulation results of the deployment scenario of the present invention, where fig. 7(a) is a minimum exposure path at a sector sensing angle of 45 °, fig. 7(b) is a minimum exposure path at a sector sensing angle of 90 °, fig. 7(c) is a minimum exposure path at a sector sensing angle of 120 °, and fig. 7(d) is a minimum exposure path after optimal deployment of an optimal deployment point.
The invention aims at the network optimization deployment of the directed sensor, and the used directed sensor is a fan-shaped sensing area, so the sensing angle can be adjusted, and the simulation sets the fan-shaped sensing angles to be 45 degrees, 90 degrees and 120 degrees respectively so as to verify the influence of different sensing angles on the coverage quality.
The curves in fig. 7(a), (b), and (c) are minimum exposure paths searched by the particle swarm algorithm. Fig. 7(a) demonstrates that the minimum exposure path can be effectively searched when the sector sensing angle is 45 °; fig. 7(b) demonstrates that the minimum exposure path can be effectively searched when the sector sensing angle is 90 °; fig. 7(c) demonstrates that the minimum exposure path can be effectively searched when the sector sensing angle is 120 °; fig. 7(d) shows that the fan sensing angle is 90 °, and fig. 7(d) demonstrates that the present invention finds not only the minimum exposure path but also the optimal deployment point, and the dashed box in fig. 7(d) is the directional sensor redeployed at the optimal deployment point.
According to the results, the particle swarm optimization can effectively search the minimum exposure path, and after the directional sensor is redeployed at the searched optimal deployment point, the originally and completely exposed minimum exposure path can pass through the deployment area of the directional sensor, namely, the exposure value is increased, and the coverage rate of the directional sensor is improved; and by comparing fig. 7(a), (b) and (c), it is found that as the fan sensing angle increases, the minimum exposure path becomes shorter and the coverage is improved. Therefore, the directed sensor network optimized deployment system and the directed sensor network optimized deployment method provided by the invention can effectively search the minimum exposure path by utilizing the particle swarm algorithm, can find the optimal deployment point based on the minimum exposure path and complete the optimized deployment at the point, can effectively improve the coverage condition of the deployment area, and improve the coverage quality.
The invention realizes the optimized deployment of the directed sensor, obviously improves the minimum exposure path, improves the coverage rate, is not limited to a fixed angle, can set the sector sensing angle of the sensor from 45 degrees to 120 degrees, and can realize the optimized deployment of the directed sensor network.
In conclusion, the invention solves the defects of large calculated amount, low searching speed and high complexity of the existing directed sensor network. The deployment system is sequentially connected end to end by an initialization random deployment module, a weighted discretization module, a minimum exposure path searching module and an optimization deployment module, so that the coverage quality of a deployment area is improved. The deployment method comprises the steps of determining a deployment area, randomly and initially deploying the sensor, and setting a minimum exposure target value; searching a minimum exposure path by using a particle swarm algorithm; determining an optimal deployment point and deploying a directional sensor; and obtaining an optimal deployment scheme. The method uses the particle swarm algorithm to search the minimum exposure path, finds the grid center point corresponding to the line segment with the minimum exposure on the path as the optimal deployment point and deploys the directional sensor, effectively improves the minimum exposure path, improves the deployment area coverage rate, has the advantages of high search speed, simple and convenient calculation and high efficiency, and is used for the optimal deployment of the directional sensor network.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. An optimized deployment system of a directed sensor network comprises an initialized random deployment module and an optimized deployment module, wherein the initialized random deployment module collects environmental information of a deployment area and determines an area suitable for deployment, and the optimized deployment module outputs an optimal deployment scheme of the directed sensor network; between the two modules, there is an optimized deployment preprocessing link, which is characterized in that: the optimized deployment preprocessing link comprises a weighted discretization module and a minimum exposure path searching module; the initialization random deployment module, the weighted discretization module, the minimum exposure path searching module and the optimized deployment module are sequentially connected end to end; the method comprises the steps that an initialization random deployment module abstracts a sensor deployment area into a rectangular area, directional sensors are initially and randomly deployed in the area, and deployment position information of each directional sensor is input into a weighted discretization module after deployment is finished; the weighted discretization module divides the deployment area into fine grids, generates a weighted undirected graph, calculates the exposure and transmits the exposure information to the minimum exposure path searching module; the minimum exposure path searching module searches a minimum exposure path by using a particle swarm optimization algorithm, performs discretization operation on the minimum exposure path and transmits the discretization operation to the optimized deployment module; and the optimal deployment module finds a grid center point corresponding to the line segment with the minimum exposure and sets the grid center point as an optimal deployment point, relocates the directional sensor to the point, and calculates the minimum exposure value of the relocated system until the value meets a set target value to obtain an optimal deployment scheme of the directional sensor network.
2. The system for optimized deployment of a directed sensor network according to claim 1, wherein: the weighted discretization module divides a sensor deployment area into a fine grid and enables a detection target to move along the grid; the weighted discretization module receives the deployment position information of each sensor from the initialized random deployment module, and generates a weighted undirected graph according to the deployment position information of each sensor, wherein the edge weight value of the weighted undirected graph is an exposure value on a grid path; and calculating to obtain the exposure on the grid moving path, and outputting the exposure information to the minimum exposure path searching module.
3. The system for optimized deployment of a directed sensor network according to claim 1, wherein: the minimum exposure path searching module adopts a particle swarm optimization algorithm to search a minimum exposure path, wherein each particle is represented by a speed vector and a position vector; the two vectors are initialized to random vectors in the solution space dimension; in the iterative process of the algorithm, the particles continuously update the speed and the position of the particles towards the local optimal solution and the global optimal solution; continuously calculating the exposure by using an exposure formula until a path with the minimum total exposure is found, and calling the path with the minimum total exposure as a minimum exposure path; and outputting the dispersed minimum exposure path information to an optimized deployment module.
4. The system for optimized deployment of a directed sensor network according to claim 1, wherein: the optimized deployment module acquires minimum exposure path information from the minimum exposure path searching module; comparing the exposure values of the line segments corresponding to all the grid points to obtain a line segment with the minimum exposure in the minimum exposure path, wherein the grid center point corresponding to the line segment is set as an optimal deployment point; and redeploying the directional sensor at the optimal deployment point, continuously calculating the minimum exposure value of the system until the value meets the set target value, and outputting the optimal deployment scheme of the directional sensor network.
5. A directed sensor network optimized deployment method, which is implemented on the directed sensor network optimized deployment system of any one of claims 1 to 4, is characterized by comprising the following steps:
1) determining a deployment area, randomly and initially deploying the sensor in the deployment area, and setting a minimum exposure target value: collecting environmental information of an area to be deployed, determining an area where the directional sensor is deployed, called a deployment area, randomly determining a position where the directional sensor is deployed in the area, called an initial position, and deploying the directional sensor at the initial position; discretizing the deployment area by using a grid method, and generating a weighted undirected graph in the deployment area; setting a minimum exposure target value according to the deployment area as a judgment basis for obtaining an optimal deployment scheme;
2) searching for a minimum exposure path using a particle swarm optimization algorithm: searching a minimum exposure path in a deployment area by using a particle swarm algorithm, wherein each particle is represented by a velocity vector and a position vector, and the two vectors are initialized to be random vectors in a solution space dimension; continuously updating the speed vector and the position vector of each particle towards the local optimal solution and the global optimal solution, and simultaneously calculating the exposure by using an exposure formula until a minimum exposure path is found;
3) calculating the exposure value of the line segment where the grid point is located on the minimum exposure path: after the minimum exposure path is obtained in the step 2), calculating the exposure values of the path line segments of all the grid points on the minimum exposure path by using an exposure formula;
4) determining an optimal deployment point: comparing the exposure values of the path line segments of all the grid points obtained in the step 3), finding out the line segment with the minimum exposure value, and determining the grid center point corresponding to the line segment as the optimal deployment point on the minimum exposure path;
5) adjusting the deployment of the sensors: deploying a directional sensor at the optimal deployment point on the minimum exposure path obtained in the step 4);
6) obtaining an optimal deployment scheme: calculating the minimum exposure value of the minimum exposure path after the directional sensor is deployed in the step 5), and comparing the minimum exposure value with the target value set in the step 1); if the minimum exposure value does not meet the set target value, returning to the step 2) to continue running; and if the minimum exposure value meets the set target value, obtaining the optimal deployment scheme of the directed sensor network.
6. According to claimThe directed sensor network optimization deployment method is characterized in that: discretizing the deployment area in the step 1) refers to dividing the deployment area into fine grids, numbering the grids, wherein the grids numbered g (i) represent the ith grid according to the numbering sequence of the grids numbered g (i), i is 1,2 … …, i is 1max，imaxThe maximum value is the maximum value in the serial numbers of the divided grids; if the coverage area of the grid by the sensing range of any directional sensor node S in the deployment area exceeds half, the grid is considered to be covered.
7. The method for optimized deployment of a directed sensor network according to claim 5, wherein: the weighted undirected graph in the step 1) refers to a line connecting any grid center point in the deployment area with grid center points in eight adjacent directions, and the exposure is used as the edge weight of the line segment after the line is connected.
8. The method for optimized deployment of a directed sensor network according to claim 5, wherein: the exposure calculation in the step 2) is represented by using the field intensity of any grid point of the directional sensor in the deployment area and the path line length corresponding to the grid point, wherein the field intensity has two representing methods, namely the covering field intensity of the whole sensor and the maximum covering intensity of the sensor.
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